During the next funding period, we will examine important questions in the analysis of censored survival data. The issues we will study arise frequently in cohort and epidemiologic studies in cancer research. More specifically, we will investigate the following topics: 1. Censored data regression methods which accommodate missing, mismeasured, or auxiliary information in either outcome or explanatory variables. These methods will also make unbiased inference possible in survival data with certain types depended right censoring. 2. Significance tests and regression diagnostics in the proportional hazards regression model using weighted generalized residuals. 3. Semiparametric regression models for the mean function of longitudinal measurements subject to censoring. We will pay particular attention to inference for repeated quality of life measurements among long term survivors of cancer, and to the regression analysis of cumulative incidence and conditional survivor functions. 4. Simple and computationally efficient methods for estimating the dependence of hazard and survivor functions on a set of covariates. We will examine both semiparametric alternatives to proportional hazards regression, such as proportional odds regression, and nonparametric methods that require minimal assumptions about the change of a hazard as a function along both the time and covariate axes.